91 results about "Bayesian neural networks" patented technology

The invention discloses a wind power short-term power prediction method based on a relative error entropy evaluation method. The wind power short-term power prediction method comprises the following steps of: 1, acquiring historical data, and pre-treating the historical data to produce various training samples; 2, dynamically selecting the training samples, and establishing a bayesian neural network prediction model, an error feedback weighing time sequence prediction model and a wind power prediction unbiased grey verhulst prediction model; 3, respectively carrying out continuous prediction by adopting the three prediction models ten days ago from a prediction day; 4, respectively counting a relative error of each group of prediction data obtained in the step three, calculating an entropy and a variation degree coefficient of each group of relative error, and calculating a weight coefficient; 5, adopting the three prediction models to respectively carry out wind power prediction on the prediction day, and obtaining three groups of prediction data; and 6, carrying out combined prediction on the weight coefficient and the three groups of prediction data obtained in the step five to obtain a wind power short-term power prediction result. With the adoption of the wind power short-term power prediction method, the problem of determining the weight coefficient of combined prediction is solved, and the accuracy of wind power prediction can be improved.

A trained model is described in the form of a Bayesian neural network (BNN) which provides a quantification of its inference uncertainty during use and which is trained using marginal likelihood maximization. A Probably Approximately Correct (PAC) bound may be used in the training to incorporate prior knowledge and to improve training stability even when the network architecture is deep.